Cisco Technology, Inc.

Google Inc.

Omid Madani

Navindra Yadav

Ali Parandehgheibi

Ellen Christine Scheib

Vimalkumar Jeyakumar

Ashutosh Kulshreshtha

Mohammadreza Alizadeh Attar

Khawar Deen

Shih-Chun Chang

Hai Trong Vu

Jackson Ngoc Ki Pang

Supreeth Hosur Nagesh Rao

Abhishek Ranjan Singh

Varun Sagar Malhotra

Shashidhar R Gandham

Andy Sloane

Vimal Jeyakumar

Anubhav Gupta

Rohit Chandra Prasad

Hoang Viet Nguyen

Sunil Gupta

Michael Standish Watts

Roberto Fernando Spadaro

Bharathwaj Sankara Viswanathan

Fnu Rachita Agasthy

Duane Thomas Barlow

Kai Chang

George Dan Toderici

David A Ross

Sanketh Shetty

Ruei-Sung Lin

Balakrishnan Varadarajan

Weilong Yang

David G King

Apostol Ivanov Natsev

Weifei Zeng

Paul Mach

John Burge

Clifford Samaniego

Alexander Joshua Frank

Kenji Arai

Sayeed Mohammed Tasnim

Yash Vipul Doshi

Shashi Gandham

Thanh Trung Ngo

Thanh Nhan Nguyen

Maxwell Aaron Mechanic

Chakradhar Reddy Vangeti

Sameer Salim Mahomed Ali Ladiwala

Sasidhar Evuru

Sai Ankith Averineni

Thanh Nhan Thi Nguyen

Sunil Kumar Gupta

An example method includes detecting, using sensors, packets throughout a datacenter. The sensors can then send packet logs to various collectors which can then identify and summarize data flows in the datacenter. The collectors can then send flow logs to an analytics module which can identify the status of the datacenter and detect an attack.

System for monitoring and managing datacenters

This disclosure generally relate to a method and system for mapping application dependency information. The present technology relates techniques that enable user-adjustable application dependency mapping of a network system. By collecting internal network data using various sensors in conjunction with external user inputs, the present technology can provide optimized application dependency mapping using user inputs.

System and method for user optimized application dependency mapping

An application and network analytics platform can capture telemetry from servers and network devices operating within a network. The application and network analytics platform can determine an application dependency map (ADM) for an application executing in the network. Using the ADM, the application and network analytics platform can resolve flows into flowlets of various granularities, and determine baseline metrics for the flowlets. The baseline metrics can include transmission times, processing times, and/or data sizes for the flowlets. The application and network analytics platform can compare new flowlets against the baselines to assess availability, load, latency, and other performance metrics for the application. In some implementations, the application and network analytics platform can automate remediation of unavailability, load, latency, and other application performance issues.

Application performance monitoring and management platform with anomalous flowlet resolution

This disclosure provides solutions for automatically grouping network devices (e.g., endpoints) into clusters based on device characteristics. In some aspects, the disclosed technology also provides solutions for generating user selectable queries based on cluster characteristics. A process of the disclosed technology can include steps for identifying one or more device characteristics associated with a first network device, identifying one or more cluster characteristics for each of a first cluster and a second cluster, and comparing the device characteristics associated with the first network device with the one or more cluster characteristics for the first cluster and the second cluster. The process can further include steps for adding the first network device to the first cluster based on the cluster characteristics for the first cluster and the device characteristics for the first network device. Systems and machine-readable media are also provided.

Endpoint cluster assignment and query generation

Systems and methods are provided for automatically discovering applications/clusters in a network and mapping dependencies between the applications/clusters. A network monitoring system can capture network flow data using sensors executing on physical and/or virtual servers of the network and sensors executing on networking devices connected to the servers. The system can determine a graph including nodes, representing at least the servers, and edges, between pairs of the nodes of the graph indicating the network flow data includes one or more observed flows between pairs of the servers represented by the pairs of the nodes. The system can determine a dependency map, including representations of clusters of the servers and representations of dependencies between the clusters, based on the graph. The system can display a first representation of a first cluster of the dependency map and information indicating a confidence level of identifying the first cluster.

MDL-based clustering for dependency mapping

The present disclosure relates to methods, systems, and non-transitory computer readable media for discovering policy scopes within an enterprise network and managing network policies for discovered policy scopes. In one aspect, a method includes identifying one or more communities of devices in an enterprise network; defining, from the one or more communities of devices, policy scopes in the enterprise network; generating a hierarchical representation of the policy scopes; identifying, based on the hierarchical representation of the policy scopes, one or more policies governing traffic flow between devices associated with each of the policy scopes; and managing application of the one or more policies at the devices.

Scope discovery and policy generation in an enterprise network

This disclosure generally relates to a method and system for generating a communication graph of a network using an application dependency mapping (ADM) pipeline. In one aspect of the disclosure, the method comprises receiving network data (e.g., flow data and process information at each node) from a plurality of sensors associated with a plurality of nodes of the network, determining a plurality of vectors and an initial graph of the plurality of nodes based upon the network data, determining similarities between the plurality of vectors, clustering the plurality of vectors into a plurality of clustered vectors based upon the similarities between the plurality of vectors, and generating a communication graph of the network system based upon the plurality of clustered vectors.

Generate a communication graph using an application dependency mapping (ADM) pipeline

Facilitating of content entity annotation while maintaining joint quality, coverage and/or completeness performance conditions is provided. In one example, a non-transitory computer-readable medium comprises computer-readable instructions that, in response to execution, cause a computing system to perform operations. The operations include aggregating information indicative of initial entities for content and initial scores associated with the initial entities received from one or more content annotation sources and mapping the initial scores to respective values to generate calibrated scores. The operations include applying weights to the calibrated scores to generate weighted scores and combining the weighted scores using a linear aggregation model to generate a final score. The operations include determining whether to annotate the content with at least one of the initial entities based on a comparison of the final score and a defined threshold value.

Facilitating content entity annotation while satisfying joint performance conditions

Disclosed herein is a multi-level analysis for determining a root cause of a network problem by performing a first level of the multi-level process that includes collecting data from one or more network components, generating a set of system metrics where each system metric of the set representing a portion of the data, ranking the set of system metrics based on a level of correlation of each system metric to the network problem to yield a ranked set of system metrics, and providing a visual representation of the first level of the multi-level process. A second level of the multi-level process includes receiving an input identifying one or more of the ranked set of system metrics to be excluded from analysis and performing a conditional analysis using only ones of the set of system metrics that are not identified for exclusion.

System and method for network root cause analysis

Growing community of inventors

San Carlos, CA, United States of America

Omid Madani